I built a 12-state accountability layer for AI agents

Description and use

COGEXT tracks AI agent commitments through 12 states, ensuring atomic transitions and deduplication.

Problem

AI agents generate commitments but fail to track or fulfill them, leading to forgotten promises and unreliable behavior.

Outcome

COGEXT provides a robust, deterministic accountability layer that ensures commitments are tracked, deduplicated, and verified, reducing missed deadlines and improving agent reliability.

Workflow

• 1. Receive agent output
• 2. Classify text for genuine commitments (confidence ≥0.5)
• 3. Extract commitment fields with Groq llama-3.3-70b in JSON mode
• 4. Validate extraction against Pydantic schema
• 5. Normalize deadlines deterministically
• 6. Compute idempotency key and insert commitment via PostgreSQL RPC
• 7. Track state transitions through 12 states using a single RPC function
• 8. Aggregate evidence to determine fulfillment
• 9. Emit immutable events and audit logs

Evidence

All state mutations go through a single PostgreSQL RPC function. Not direct UPDATEs from application code

Source

We validate everything against Pydantic schemas before touching the database. No exceptions.

Source

The hour window is deliberate: it deduplicates within a session while still allowing the same promise to be legitimately re-made days later.

Source